The advent of Single Instruction Multiple Data (SIMD) instructions in modern processors has revolutionized data processing by enabling simultaneous computation across multiple data elements. While database systems have extensively adopted SIMD for traditional operations, its potential for complex event pattern matching remains largely unexplored. This paper presents a novel approach that bridges this gap through bit-parallel processing enhanced with AVX-512 vectorization. Our approach encodes event streams into compact bit sequences, where each bit corresponds to a time slice, and an event's presence is marked by a 1-bit when its timestamp falls within the respective slice. This representation enables the formulation of bit-parallel operations that natively enforce complex event constraints, including temporal window requirements and event ordering relationships. We develop a family of bit-parallel algorithms that leverage this representation for continuous event matching, and further optimize their performance through SIMD vectorization (AVX-512 instructions) to exploit modern hardware parallelism. Experimental evaluations on both real-world and synthetic datasets demonstrate the superiority of our method, achieving at least 35.7x improvement in query efficiency compared to state-of-the-art alternatives.

Graph clustering plays a crucial role in analyzing graph data. Among various clustering techniques, Structural Graph Clustering (SCAN) stands out for its ability to not only identify clusters but also recognize hubs and outliers. Evaluating the robustness of graph clustering methods is essential, and manipulating SCAN is an effective approach for this purpose. However, the scarcity of efficient manipulation techniques for SCAN poses a significant challenge, hindering the development of robust structural graph clustering algorithms. To address this issue, we investigate the problem of efficiently manipulating SCAN by strategically inserting a maximum number of$\tau$edges to maximize the increment of the$\epsilon$-neighborhood$(\Delta N_{\epsilon}[t])$under Jaccard similarity around a given target vertex$t$. This problem termed Maximum$\epsilon$. Neighborhood (MaxN), is NP-hard and non-monotonic. To tackle this, we first develop efficient edge insertion strategies and present a basic algorithm MaxNS. Then, we propose an effective algorithm, IncreMaxNS, that incrementally calculates the vertex costs in each round. Furthermore, we explore a pruning and optimization algorithm, called pMaxNS, which uses a novel strategy to select a valid candidate vertex set based on the budget$\tau$, eliminating the need to evaluate all vertices in the graph. Finally, we conduct extensive experiments on seven real-world datasets, which demonstrate that our algorithm pMaxNS significantly improves manipulation efficiency, achieving 1-1.5 orders of magnitude speedup compared to the state-of-the-art approach, while consistently delivering high-quality results.

This paper proposes a new task in the field of Answering Subjective Induction Question on Products (SUBJPQA). The answer to this kind of question is non-unique, but can be interpreted from many perspectives. For example, the answer to ‘whether the phone is heavy’ has a variety of different viewpoints. A satisfied answer should be able to summarize these subjective opinions from multiple sources and provide objective knowledge, such as the weight of a phone. That is quite different from the traditional QA task, in which the answer to a factoid question is unique and can be found from a single data source. To address this new task, we propose a three-steps method. We first retrieve all answer-related clues from multiple knowledge sources on facts and opinions. The implicit commonsense facts are also collected to supplement the necessary but missing contexts. We then capture their relevance with the questions by interactive attention. Next, we design a reinforcement-based summarizer to aggregate all these knowledgeable clues. Based on a template-controlled decoder, we can output a comprehensive and multi-perspective answer. Due to the lack of a relevant evaluated benchmark set for the new task, we construct a large-scale dataset, named SupQA, consisting of 48,352 samples across 15 product domains. Evaluation results show the effectiveness of our approach.

Recently a lot of research works have focused on geo-social group queries for group-based activity planning and scheduling in location-based social networks (LBSNs), which return a social cohesive user group with a spatial constraint. However, existing studies on geo-social group queries assume the users are stationary whereas in real LBSN applications all users may continuously move over time. Thus, in this paper we in-vestigate the problem of continuous geo-social groups monitoring (CGSGM) over moving users. A challenge in answering CGSGM queries over moving users is how to efficiently update geo-social groups when users are continuously moving. To address the CGSGM problem, we first propose a baseline algorithm, namely Baseline-BB, which recomputes the new geo-social groups from scratch at each time instance by utilizing a branch and bound (BB) strategy. To improve the inefficiency of BB, we propose a new strategy, called common neighbor or neighbor expanding (CNNE), which expands the common neighbors of edges or the neighbors of users in intermediate groups to quickly produce the valid group combinations. Based on CNNE, we propose another baseline algorithm, namely Baseline-CNNE. As these baseline algorithms do not maintain any intermediate results to facilitate further query processing, we develop an incremental algorithm, called incremental monitoring algorithm (IMA), which maintains the support, common neighbors and the neighbors of current users when exploring possible user groups for further updates and query processing. Finally, we conduct extensive experiments using three real datasets to validate our ideas and evaluate the proposed algorithms,

Informative representation of road networks is essential to a wide variety of applications on intelligent transportation systems. In this article, we design a new learning framework, called Representation Learning for Road Networks (RLRN), which explores various intrinsic properties of road networks to learn embeddings of intersections and road segments in road networks. To implement the RLRN framework, we propose a new neural network model, namely Road Network to Vector (RN2Vec), to learn embeddings of intersections and road segments jointly by exploring geo-locality and homogeneity of them, topological structure of the road networks, and moving behaviors of road users. In addition to model design, issues involving data preparation for model training are examined. We evaluate the learned embeddings via extensive experiments on several real-world datasets using different downstream test cases, including node/edge classification and travel time estimation. Experimental results show that the proposed RN2Vec robustly outperforms existing methods, including (i) Feature-based methods : raw features and principal components analysis (PCA); (ii) Network embedding methods : DeepWalk, LINE, and Node2vec; and (iii) Features + Network structure-based methods : network embeddings and PCA, graph convolutional networks, and graph attention networks. RN2Vec significantly outperforms all of them in terms of F1-score in classifying traffic signals (11.96% to 16.86%) and crossings (11.36% to 16.67%) on intersections and in classifying avenue (10.56% to 15.43%) and street (11.54% to 16.07%) on road segments, as well as in terms of Mean Absolute Error in travel time estimation (17.01% to 23.58%).

Road network is a basic component of intelligent transportation systems (ITS) in smart city. Informative representation of road networks is important as it is essential to a wide variety of ITS applications. In this paper, we propose a neural network representation learning model, namely Intersection of Road Network to Vector (IRN2Vec), to learn embeddings of road intersections that encode rich information in a road network by exploring geo-locality and intrinsic properties of intersections and moving behaviors of road users. In addition to model design, several issues unique to IRN2Vec, including data preparation for model training and various relationships among intersections, are examined. We evaluate the learned embeddings via extensive experiments on three real-world datasets using three downstream test cases, including prediction of traffic signals and crossings on intersections and travel time estimation. Experimental results show that the proposed IRN2Vec outperforms three existing methods, DeepWalk, LINE and Node2vec, in terms of F1-score in predicting traffic signals (22.21% to 23.84%) and crossings (8.65% to 11.65%), and mean absolute error (MAE) in travel time estimation (9.87% to 19.28%).